1 research outputs found
Investigating renewable energy systems using artifcial intelligence techniques
This research investigated applying Artificial Intelegence (AI) and Machine Learning (ML)
to renewable energy through three studies. The first study characterized and mapped the recent
research landscape in the field of AI applications for various renewable energy systems using
Natural Language Prcoessing (NLP) and ML models. It considered published documetns at Scopus
database in the period (2000-2021). The second study built hybrid Catboost-CNN-LSTM
architecture pipeline to predict an industrial-scale biogas plant’s daily biogas production and
investigate the feedstock components importance on it. The third study investigated prediciting
biogas yield of various subtrates and the significance of each organic component (carbohydrates,
proteins, fats/lipids, and legnin) in biogas production using hybrid VAE-XGboost model.
The first study showed seven main metatopics and ascent of "deep learning (DL)" as a
prominent methodology led to an increase in intricate subjects, including the optimization of power
costs and the prediction of wind patterns. Also, a growing utilization of DL approaches for the
analysis of renewable energy data, particularly in the context of wind and solar photovoltaic
systems. The research themes and trends observed in the first study signify substantial recent
investments in advanced AI learning techniques. The developed Catboost-CNN-LSTM pipeline
achived a significant results and presented a superior approach when compared to previous
relevant studies by eliminating the requirement for feature engineering, enabling direct prediction
of biogas yield without the need for converting it into a classification task. The VAE-XGboost
pipeline could ovcercome data limitation in the field and produced significant results. It has shown
that the "fats" category is the most influential group on the methane production in biogas plants,
however, “proteins” illustrated the lowest impact on biogas production